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Learning Regular Omega Languages

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Algorithmic Learning Theory (ALT 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8776))

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Abstract

We provide an algorithm for learning an unknown regular set of infinite words, using membership and equivalence queries. Three variations of the algorithm learn three different canonical representations of omega regular languages, using the notion of families of dfas. One is of size similar to L $, a dfa representation recently learned using L* [7]. The second is based on the syntactic forc, introduced in [14]. The third is introduced herein.We show that the second can be exponentially smaller than the first, and the third is at most as large as the first two, with up to a quadratic saving with respect to the second.

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Author information

Authors and Affiliations

  1. Yale University, New Haven, Connecticut, USA

    Dana Angluin

  2. University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Dana Fisman

Authors
  1. Dana Angluin
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  2. Dana Fisman
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Editor information

Editors and Affiliations

  1. Montanuniversitaet Leoben, 8700, Leoben, Austria

    Peter Auer

  2. Department of Philosophy, King’s College, WC2R 2LS, London, UK

    Alexander Clark

  3. Division of Computer Science, Hokkaido University, N-14, W-9, 060-0814, Sapporo, Japan

    Thomas Zeugmann

  4. Department of Computer Science, University of Regina, S4S 0A2, Regina, SK, Canada

    Sandra Zilles

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Angluin, D., Fisman, D. (2014). Learning Regular Omega Languages. In: Auer, P., Clark, A., Zeugmann, T., Zilles, S. (eds) Algorithmic Learning Theory. ALT 2014. Lecture Notes in Computer Science(), vol 8776. Springer, Cham. https://doi.org/10.1007/978-3-319-11662-4_10

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  • DOI: https://doi.org/10.1007/978-3-319-11662-4_10

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11661-7

  • Online ISBN: 978-3-319-11662-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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